Book Image

Learning YARN

By : Akhil Arora, Shrey Mehrotra, Shreyank Gupta
Book Image

Learning YARN

By: Akhil Arora, Shrey Mehrotra, Shreyank Gupta

Overview of this book

Today enterprises generate huge volumes of data. In order to provide effective services and to make smarter and more intelligent decisions from these huge volumes of data, enterprises use big-data analytics. In recent years, Hadoop has been used for massive data storage and efficient distributed processing of data. The Yet Another Resource Negotiator (YARN) framework solves the design problems related to resource management faced by the Hadoop 1.x framework by providing a more scalable, efficient, flexible, and highly available resource management framework for distributed data processing. This book starts with an overview of the YARN features and explains how YARN provides a business solution for growing big data needs. You will learn to provision and manage single, as well as multi-node, Hadoop-YARN clusters in the easiest way. You will walk through the YARN administration, life cycle management, application execution, REST APIs, schedulers, security framework and so on. You will gain insights about the YARN components and features such as ResourceManager, NodeManager, ApplicationMaster, Container, Timeline Server, High Availability, Resource Localisation and so on. The book explains Hadoop-YARN commands and the configurations of components and explores topics such as High Availability, Resource Localization and Log aggregation. You will then be ready to develop your own ApplicationMaster and execute it over a Hadoop-YARN cluster. Towards the end of the book, you will learn about the security architecture and integration of YARN with big data technologies like Spark and Storm. This book promises conceptual as well as practical knowledge of resource management using YARN.
Table of Contents (20 chapters)
Learning YARN
About the Authors
About the Reviewers

Submitting a sample MapReduce application

When a MapReduce application is submitted to a Hadoop-YARN cluster, a series of events occurs in different components. In this section, we will submit a sample Hadoop-YARN application to a cluster. We will discuss the application flow with the help of snapshots and understand how the series of events occurs.

Submitting an application to the cluster

As discussed in Chapter 3, Administering a Hadoop-YARN Cluster, the yarn jar command is used to submit a MapReduce application to a Hadoop-YARN cluster. An example jar is packaged inside the Hadoop bundle. It contains sample MapReduce programs, such as word count, pi estimator, pattern search, and so on. This is shown in the following figure:

As shown in the preceding diagram, we have submitted a pi job with 5 and 10 as sample arguments. The first argument 5 denotes the number of map tasks and the second argument 10 represents the samples per map as parameters to the job.

yarn jar <jarPath> <JobName...